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Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach

Ran Li, Pan Xiao, Kaushik Dutta, Youdong Guo

TL;DR

This work tackles automatic segmentation of neuronal activity in fast 4D calcium imaging by introducing a Bayesian Uncertainty U-Net that fuses temporal correlation maps with spatial variance to produce probabilistic segmentations and uncertainty maps. Ground truth is synthetically generated via Otsu thresholding on variance maps, enabling training without manual labeling. The method demonstrates robust segmentation (Dice around 0.8) and high background accuracy, with strong reproducibility across data splits, suggesting good generalization to new datasets. Overall, the approach offers a scalable, uncertainty-aware solution for rapid neuronal activity detection in behavioral neuroscience using light-sheet calcium imaging.

Abstract

Fluorescence Microcopy Calcium Imaging is a fundamental tool to in-vivo record and analyze large scale neuronal activities simultaneously at a single cell resolution. Automatic and precise detection of behaviorally relevant neuron activity from the recordings is critical to study the mapping of brain activity in organisms. However a perpetual bottleneck to this problem is the manual segmentation which is time and labor intensive and lacks generalizability. To this end, we present a Bayesian Deep Learning Framework to detect neuronal activities in 4D spatio-temporal data obtained by light sheet microscopy. Our approach accounts for the use of temporal information by calculating pixel wise correlation maps and combines it with spatial information given by the mean summary image. The Bayesian framework not only produces probability segmentation maps but also models the uncertainty pertaining to active neuron detection. To evaluate the accuracy of our framework we implemented the test of reproducibility to assert the generalization of the network to detect neuron activity. The network achieved a mean Dice Score of 0.81 relative to the synthetic Ground Truth obtained by Otsu's method and a mean Dice Score of 0.79 between the first and second run for test of reproducibility. Our method successfully deployed can be used for rapid detection of active neuronal activities for behavioural studies.

Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach

TL;DR

This work tackles automatic segmentation of neuronal activity in fast 4D calcium imaging by introducing a Bayesian Uncertainty U-Net that fuses temporal correlation maps with spatial variance to produce probabilistic segmentations and uncertainty maps. Ground truth is synthetically generated via Otsu thresholding on variance maps, enabling training without manual labeling. The method demonstrates robust segmentation (Dice around 0.8) and high background accuracy, with strong reproducibility across data splits, suggesting good generalization to new datasets. Overall, the approach offers a scalable, uncertainty-aware solution for rapid neuronal activity detection in behavioral neuroscience using light-sheet calcium imaging.

Abstract

Fluorescence Microcopy Calcium Imaging is a fundamental tool to in-vivo record and analyze large scale neuronal activities simultaneously at a single cell resolution. Automatic and precise detection of behaviorally relevant neuron activity from the recordings is critical to study the mapping of brain activity in organisms. However a perpetual bottleneck to this problem is the manual segmentation which is time and labor intensive and lacks generalizability. To this end, we present a Bayesian Deep Learning Framework to detect neuronal activities in 4D spatio-temporal data obtained by light sheet microscopy. Our approach accounts for the use of temporal information by calculating pixel wise correlation maps and combines it with spatial information given by the mean summary image. The Bayesian framework not only produces probability segmentation maps but also models the uncertainty pertaining to active neuron detection. To evaluate the accuracy of our framework we implemented the test of reproducibility to assert the generalization of the network to detect neuron activity. The network achieved a mean Dice Score of 0.81 relative to the synthetic Ground Truth obtained by Otsu's method and a mean Dice Score of 0.79 between the first and second run for test of reproducibility. Our method successfully deployed can be used for rapid detection of active neuronal activities for behavioural studies.

Paper Structure

This paper contains 13 sections, 9 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Data Acquisition and Dataset Description
  4. Synthetic Ground Truth Generation by Adaptive Thresholding
  5. Temporal information Extraction by Correlation
  6. Neural Network Architecture
  7. Networks
  8. Training
  9. Evaluation Metrics
  10. Results and Discussion
  11. Semantic segmentation performance
  12. Evaluation of Reproducibility
  13. Conclusion

Figures (5)

  • Figure 1: Objective-Coupled Planar Illumination (OCPI) microscopy
  • Figure 2: Overview of the Pipeline
  • Figure 3: Bayesian Uncertainty U-Net Architecture
  • Figure 4: The depiction of the performance of the network represented on a single frame (a) Raw Image representing a frame from the 4D spatio-temporal data (b) Segmentation Probability Map obtained from the B-U2Net (c) Epistemic Uncertainty Map of the network (d) Overlaying of the results of the probability segmentation maps obtained by the network and the synthetic Ground Truth obtained by Otsu's Threshold on the representative frame (e) Segmentation Map obtained when the network is trained with the first split of the dataset (f) Segmentation Map obtained when the network is trained with the second split of the dataset (g) The first and second segmnetation on the same representative frame is overlaid to signify the reproducibility of the algorithm
  • Figure 5: Correlation between the Dice Score and Epistemic Uncertainty